Report Ref · NV-2026-002
Period · Q2 2026
Prepared by Navedas Intelligence
Embedded AI Engineering · CX & Operations

We build your Ops.You own the AI.

Your AI ships refunds your policy denies and resolutions your auditor can't reconstruct. Two field engineers embed, build the layer you own, and leave. CSAT.AI in production since 2021, patent-backed.

Audit starts at $500 · Results in 48 hrs

Recovery Report
CX Operations · Q3 Report
RPT-2026-Q3-014
1,000 decisions
48-hour turnaround
ID Decision Policy Violated Exposure
D-01 Refund approval RETURN-001 §3.2 $1,249
D-02 Refund approval RETURN-001 §3.2 $987
D-03 Discount stack DISCOUNT-002 §2.1 $340
D-04 Escalation override SLA-MATRIX-V3 §4.7 $4,200
D-05 VIP retention credit LTV-OVERRIDE §1.3 $12,400
D-06 Goodwill credit GOODWILL-002 §1.3 $215
D-07 Chargeback dispute FRAUD-003 §1.1 $2,890
D-08 Shipping reship FULFILL-004 §3.5 $476
··· 312 more decisions
Total quarterly exposure
$340,125

Composite. Line items anonymized from four engagements.

Recovery
$120K+
per workflow / quarter
Audit Turnaround
48 hrs
data to Recovery Report
Citation Accuracy
99.7%
per decision, audit-ready
Patent
US 11,748,414
granted 2023 · active until 2042

Drawn from live engagements across CX, fulfillment, and compliance workloads. Recovery figures vary by ticket volume, policy complexity, and existing governance maturity.

Engagement Model

We don't ship software.
We embed engineers.

Five stages. Three commercial milestones. One engagement. Two field engineers embed in your operation: onsite, hybrid, or remote based on your data residency rules. You own the AI when we leave.

Onshore-only delivery available for HIPAA, FedRAMP, and EU-sovereignty workloads.
01·48h
Diagnose
1,000 of your decisions mapped to your policies. You see where margin leaks.
02·CO-BUILD
Two FDEs embed
Field engineers build the AI for your use case, harden it for your stack, scale it to production volume.
03·OPERATE
You own the AI
Your team runs it on infrastructure you already own. We tune models, ship monthly audit packs, handle incidents.
Workforce Architecture

You don't need a smarter chatbot.
You need an engineered workforce.

Most AI deployments give one model broad access to your APIs and hope policy emerges from the prompt. We don't. Navedas structures your automation as a multi-agent organization inside your VPC. Each agent has a defined role, a sandboxed scope, and an exposure budget.

Intake Agent

Receives and classifies

Receives the inbound event, classifies intent, and locks the record in your helpdesk to prevent race conditions. Every interaction enters one door.

Domain Agents

Scoped to your operation

Refunds, retention, escalation, fulfillment: each agent references the Context Graph to construct a resolution payload. None has cross-domain access. Authorization is structural, not prompt-based.

Budget Layer

Caps authorized exposure

Each agent operates with a defined dollar cap per quarter. A refund agent at $50K of governed decisions is a different operational posture than the same agent with no cap. The runaway-cost case never reaches the customer.

Three agents or thirty. The structure is the same. Each agent operates with the authority you defined. Nothing else.

● Proven, then productized

We built CSAT.AI first. Then we built it for you.

Four years inside customer-experience operations before we shipped the platform. The Decision Gate isn't a thesis. It's the load-bearing engine our first customers have been running since 2021.

Deployed inside $400M Apparel  ·  $2B Fintech  ·  $280M D2C  ·  $1.4B Retail  ·  $1.8B Marketplace
Landit
pyypl
logicbroker
Rotho
ElevatePay
dyme
Bombas
The Exposure

This is happening inside your operation right now

Every hour, decisions ship without policy enforcement. You won't see the cost until the P&L review.

$340K
Quarterly · ungoverned AI refunds

Your AI agent approved a $1,249 refund your policy says to deny

74-day refund, 60-day policy. The bot read the customer's frustration, not your SOP.

$84K
Quarterly · losses the team had no tool to prevent

A hostile call, a policy that changed last Tuesday, and no one told the floor

BBB threat, 90 seconds to respond, an updated SOP no one pushed to the screen. The gap wasn't their judgment. It was ours.

3.2%
Of your refund volume · serial-claimant abuse

Serial claimants already know your Customer 360 can't stop them

Four accounts, one household, twelve refunds in 90 days. The Customer 360 sees four separate customers. The bot approved a fifth.

4–6 hrs
Per audit inquiry · unquantified liability

Your auditor asked 'why' and the system never captured the answer

Six hours digging through Slack because no one built a place to record the 'why' in the first place.

The Governance Layer

One engine governs every
decision-maker in your organization

Same gates. Same trace. Same ledger. Human and AI, one engine.

AI Agent

When Your AI Agent Acts

Every refund, discount, or escalation hits a policy gate before it commits. Violations never reach the customer.

Live Example

AI-Agent-07 → $1,249 refund → RETURN-001 §3.2 (74d > 60d) → FRAUD-003 §1.1 (3rd claim in 90d)

Blocked before customer sees the response
Human Agent

When Your Human Agent Acts

Under pressure, your agent sees the right policy, the fiscal impact, and a defensible alternative. All on one screen.

Live Example

Human-Sarah.K → 35% discount → DISCOUNT-002 §2.1 (35% > 20% max) → LTV $2,400 does not qualify

Agent sees: "20% max per DISCOUNT-002 §2.1. Offer store credit alternative."
The Governance Pipeline
Interaction Enters
From any channel: chat, phone, email, AI bot
Policy Gates Fire
Identity → Eligibility → Fiscal → Fraud → Sentiment
Precedent + Policy Lookup
Graph search: similar tickets + matching policy nodes
Deterministic Verdict
Approved, Blocked, or Escalated. With full citation.
Reasoning Ledger
Every decision persisted with audit-grade traceability
The Interceptor

A realtime gate between decision and action. Violations are blocked before the customer sees a response. Works identically for bots and human keystrokes.

The Reasoning Trace

Every gate, citation, and fiscal step documented. When your auditor asks "why was this denied?", the answer is one click away.

Institutional Memory

Every verified resolution becomes a precedent. After 1,000 tickets, the engine knows your playbook better than your best supervisor.

Identity-Stitched Policy Enforcement

Your CRM shows one identity. Navedas stitches linked accounts, devices, and channels, so the policy threshold fires whether the request hits the agent, the bot, or self-serve.

Guardrails ≠ Decision Gate

Your AI's guardrails
aren't a Decision Gate.

Every AI platform ships with guardrails. Here's what they miss.

Today's AI guardrails
Conflict of interestThe model vendor writes the LLM, writes the guardrails, and writes the logs. In no other regulated industry would the same entity creating decisions be the one certifying them.
AI-only coverageGoverns the bot. Your human agents and back-office have zero coverage.
No policy-node citationsRAG cites "our documentation." An auditor needs the specific policy node that governed the decision. POL-042 §3.2, not a vague paragraph.
Hallucinations under pressureAt 3 a.m., ambiguous SOP, angry customer. The model fabricates policy to resolve the conversation. Faster, not safer.
Post-hoc, not pre-actionLogs are what auditors read after a six-figure dispute. A Decision Gate intercepts before the bad decision commits.
Navedas Decision Gate
DeterministicEach decision traced to a policy node. 99.7% accuracy. No citation, no action.
Human + AI unifiedOne engine for bots, agents, fulfillment, and compliance. Same rules. Same trace.
Reasoning LedgerEvery verdict cites rule, section, clause. Auditor answers in seconds.
Context GraphSOPs converted into a live, enforceable policy graph with vector embeddings. Not keyword matching.
Compounding intelligenceEvery verified resolution becomes a precedent. The engine learns your playbook.
Engagement ≠ Subscription

This isn't a SaaS subscription.
It's an engineering engagement.

How a vendor relationship with Navedas differs from buying SaaS.

The SaaS subscription model
You configure their platformPre-built features and templates. Your operation bends to fit the tool.
Onboarding via documentationSolutions engineers schedule calls. Your team self-serves the rest.
Your data flows to their infrastructureTickets, transcripts, PII, customer records. All on a tenant in their VPC.
Renewal equals continued accessStop paying, lose the product. You own nothing at the end.
Generic policy templatesIndustry-standard playbooks. Your actual SOPs sit in a side document the model never reads.
The Navedas engagement model
We build yoursNo pre-built tool to fit into. The AI gets built around your policies, your data, your operation.
Engineers embedded in your operationTwo FDEs onsite, hybrid, or remote based on your data residency rules. Not a CSM. Not a partner referral.
The AI runs in your VPCOnshore-only deployment available for HIPAA, FedRAMP, and EU-sovereignty workloads. You own it.
Renewal equals continued engineeringStop the engagement and you still own the AI. The engine is yours, the policies are yours, the Operator Console is yours.
Your actual policies, encodedSOPs converted into a live policy graph. Auditor reads the same node the AI cited.

See the leak before you commit. Send 1,000 tickets. 48 hours. $25K minimum or your money back.

Start your $500 audit
Evidence of Citation

No citation.
No output.

Every verdict traces back to a specific policy line. If the engine can't cite the rule, it doesn't act.

Your auditor sees the rule
RETURN-001 §3.2  ·  FRAUD-003 §1.1

The exact rule, clause, and subsection behind every decision. Named, not narrated.

The Reasoning Ledger answers why

An immutable record of who decided, when, why, under which policy. Auditor answers in seconds, not weeks.

Your team makes the call

Navedas surfaces the policy and the governed alternative on one screen. Your people decide, backed by evidence.

Human Authority

Your team doesn't fix typos.
They authorize exceptions.

Standard AI uses humans as a backup when the model fails. Navedas inverts that: humans operate at the boundaries of your corporate authority, not as cleanup crew for routine decisions.

Policy blind spot
The action falls outside any policy in the Context Graph. A human decides whether to extend authority.
High-risk threshold
The action exceeds a dollar limit you defined: refunds over $500, retention offers over $1K. A human authorizes the exception.
Budget exhaustion
The agent has reached its per-quarter exposure cap. A human decides whether to extend it. Every decision is stamped to the audit trail.
Operator Console · Pending Authorization
Decision Blocked
Refund · $250 · Order #91-4872
Policy RETURN-001 §3.2 — 60-day window. Ticket age: 74 days. Exception required.
Customer Context
LTV
$14,200
Prior claims
0 of 4 flagged
Tenure
3.2 years
Governed alternative
Store credit $250
● The thesis

Six gates. Five seconds.
One verdict.

Every refund, discount, escalation, settlement, checked against your policy graph before the customer sees an answer. No citation, no output.

How It Works

From your SOPs to
defended decisions in 3 steps

50-page SOP → 100+ policy nodes in 48 hours. Live in a week. No infrastructure changes.

Step 01

Build Your Context Graph

Your SOPs, refund matrices, and escalation trees become a live policy graph, each rule enforceable, citeable, and searchable.

100+ policy nodes from your documents, ready in 48 hours
Step 02

Load Your Precedents

Your best resolved tickets become precedent nodes linked to the graph. Every new resolution sharpens the engine.

After 1,000 tickets, the engine knows your playbook better than your best supervisor
Step 03

Govern in Real-Time

Every interaction flows through the engine. Gates fire, precedents match, verdicts ship. All before the action reaches your customer.

Sub-second policy decisions on every interaction, 24/7
50-page SOP →  100+ policy nodes in 48 hours
Live in a week.  No infrastructure changes
Every decision  governed before it ships
How Governance Works

AI + Human = one policy

Every intent, human or AI, flows through one compile-time boundary before it reaches your customer.

Human Judgment
CRM Manual Override
Salesforce Zendesk Gladly
AI Execution
Bot Action
Speed Scale
Multi-Agent Runtime
Domain Agent Payload
CEO Agent Triage
Decision Engine
The Compile-Time Boundary
No Citation. No Output.
Context Graph Eval Policy-Node Match Pre-Execution Gate Reasoning Ledger
Approved
Automated API Writeback
Ledger Stamped
Blocked
Hard Compile-Time Stop
Execution Frozen
Escalated
Operator Console
Strategic Override
Solutions

Your workflow. Your policies.
One decision engine.

CX, fulfillment, compliance. One engine. Every decision traceable.

FAQ

Questions your team
is already asking

Why can't we just use the guardrails built into our chatbot or AI platform?
Guardrails are probabilistic and cover only the bot. Navedas is one deterministic engine for every decision-maker, citing policy nodes, not confidence scores.
We already have QA processes. Why do we need real-time policy enforcement?
QA catches problems weeks later. After the money's gone. Navedas intercepts before the decision commits.
How is this different from a rules engine or workflow automation?
Rules engines match keywords. Navedas reasons over LTV, claim history, fraud signals, and precedent, against your atomized SOP, not "refund > $500."
What does "No Citation, No Output" actually mean?
Every verdict must cite a policy node: RETURN-001 §3.2, DISCOUNT-002 §2.1. No citation, no action. The architectural guarantee against hallucination.
When will my next board meeting have real numbers?
48 hours to a Revenue Recovery Report. A week to live governance. 30 days to numbers you can put on a slide.
What happens if we stop the engagement?
You own the AI we built. The policy graph, the Reasoning Ledger, the Operator Console, all the precedents we trained on your data. Yours. The engagement is engineering; the engine is yours.
Built for two audiences

Ops sees one story. Your CFO sees another.

For CX & Operations

Agents don't take the blame for policy they didn't write.

The Operator Console shows agents the right threshold before they click approve, and flags bot decisions that violate policy in realtime, with the reason in plain English, right in the queue.

  • ·Policy backup for human agents
  • ·Realtime intercepts on bots and self-serve
  • ·Settlement drift caught before it compounds
For CFO & Leadership

What got recovered, and why, in a dashboard your board understands.

The Executive Dashboard tracks recovered revenue by policy area. The Reasoning Ledger reconstructs any decision in seconds, policy citation attached.

  • ·Revenue recovered, tracked weekly
  • ·Audit readiness without a fire drill
  • ·Policy gap reports for every quarter
● Delivery

48 hours to a report. A week to live governance.
30 days to numbers you can put on a slide.

Two field engineers embed in your stack. No infrastructure changes. You own the AI when we leave.

Pricing

How much revenue is your team actually leaving on the table?

Send us 1,000 of your recent tickets. In 48 hours we'll give you a specific recoverable-dollar figure, with the policy violations that caused each one. If we don't find at least $25,000 in exposure, you get your money back.

$25K minimum finding. Or your money back.
Eligible: any company running AI in customer-facing or back-office decisions. SOC 2 / HIPAA / GDPR coverage on request. NDA before data transfer.
Scoped diagnostic: fixed fee, outcome-bound. We surface the recoverable dollar figure or refund the fee.
What lands in 48 hours
01
Per-decision dollar exposure
Every off-policy decision in the 1,000-ticket sample, dollar-quantified.
02
Policy violation citations
Exact policy section and rule violated, per decision.
03
Recoverable-amount summary
Quarterly exposure with recovery path per category.
Start here
Diagnose
$500  flat fee

1,000 decisions analyzed against your policies. Revenue Recovery Report in 48 hours. Productized scope. Try the engagement before you commit.

  • 1,000 decisions analyzed
  • 48-hour turnaround
  • Money-back guarantee
  • No integration. Just a CSV.
Start your $500 audit
Build
$10K–$20K  / month engagement

Two field engineers embed for one policy area. We build that part of your operation around your actual policies; you run it. 30 days to live.

  • Two FDEs embedded for one policy area
  • Reasoning Ledger on every decision
  • Weekly exposure-reduction report
  • Operator Console for your team
Start the engagement
Operate
$80K–$250K  / year engagement

Engineers stay embedded for ongoing tune and incident response. Every policy area governed. You own the platform.

  • FDEs embedded continuously
  • Every policy area governed
  • Context Graph + Reasoning Ledger at scale
  • Executive Dashboard + Operator Console
  • Enterprise SSO, SLAs
Talk to the inventors

We're not a helpdesk replacement. Navedas runs on top of what you already use (Zendesk, Gladly, Salesforce, or your own stack) and enforces policy on every decision those systems touch.

Prepared by
Navedas Intelligence · Engineering team
Reviewed by
Founding inventors · Q2 2026
Endnotes
  1. [1] Recovery figures drawn from live engagements across CX and back-office operations. Per-engagement variance: ticket volume, policy complexity, and operational maturity.
  2. [2] CSAT.AI: in production since Q1 2021 with the founding customer cohort. Decision Gate engine load-bearing for refund, discount, escalation, and resolution workflows.
  3. [3] US Patent 11,748,414 granted 2023, active until 2042. Covers the citation-bound decision architecture described on this page.
  4. [4] $25K minimum finding methodology: 1,000 decisions analyzed against client policy graph in 48 hours. Audit refunded if recoverable exposure falls below threshold. Money-back guarantee bound at audit signoff.
  5. [5] Recovery Report shown above is a composite. Line items anonymized from four engagements. Format and structure represent the actual artifact delivered at Diagnose stage.
Document NV-2026-002 · Revision A · Q2 2026 Five exhibits · Five endnotes · One engagement

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